Increasing similarity between seismic datasets

ABSTRACT

A method for increasing similarity between a first seismic dataset and a second seismic dataset from at least one seismic survey of a subsurface. The first and second seismic datasets are migrated to a dip angle image domain. The migrated first and second seismic datasets are used in the dip angle image domain to calculate a set of decimating weights to be applied to the first seismic dataset and the second seismic dataset to maximize a similarity between the first seismic dataset and the second seismic dataset. The decimated weights are applied to the first and second seismic datasets, and an image of the subsurface is generated using the first seismic dataset and the second seismic dataset following application of the decimated weights.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of co-pending U.S. patentapplication Ser. No. 13/784,112 filed Mar. 4, 2013 for “Image domain4D-Binning Method and System”. In addition, this application claimspriority and benefit from U.S. Provisional Patent Application No.62/088,107, filed Dec. 5, 2014, for “Dip-Angle Domain 4D Processing” andU.S. Provisional Patent Application No. 62/145,519, filed Apr. 10, 2015,for “Dip Filtering for 4D Monitoring”. The entire contents of all ofthese applications are incorporated herein by reference.

TECHNICAL FIELD

Embodiments of the subject matter disclosed herein relate to methods andsystems for processing 4-dimensional (4D) seismic data to image asubsurface area during reservoir production.

BACKGROUND

Evaluation of production from a subsurface reservoir utilizesfour-dimensional (4D) processing of two seismic datasets obtained at twodifferent times, e.g., two vintages, from a given subsurface region todetermine changes in Earth properties resulting, for example, frompetroleum reservoir production. The two seismic datasets can be obtainedfrom land-based seismic surveys and marine-based seismic surveys. As theseismic surveys are conducted at different times, variations in thegeometries of the two surveys exists, which complicates the comparisonof the two seismic datasets. These variations in geometry occur inparticular in marine-based seismic surveys.

Marine seismic data acquisition and processing generate an image of ageophysical structure, i.e., subsurface, under the seafloor. While thisimage does not provide a precise location for oil and gas reservoirs, itsuggests, to those trained in the field, the presence or absence of oiland/or gas reservoirs. Thus, providing a high-resolution image of thesubsurface is an ongoing process for the exploration of naturalresources, including, among others, oil and/or gas.

During a seismic gathering process, as shown in FIG. 1, a vessel 10 towsan array of seismic receivers 11 located on streamers 12. The streamersmay be disposed horizontally, i.e., lying at a constant depth relativeto the ocean surface 14, or may have spatial arrangements other thanhorizontal, e.g., variable-depth arrangement. The vessel 10 also tows aseismic source array 16 configured to generate a seismic wave 18. Theseismic wave 18 propagates downward, toward the seafloor 20, andpenetrates the seafloor until, eventually, a reflecting structure 22(reflector) reflects the seismic wave. The reflected seismic wave 24propagates upward until it is detected by receiver 11 on streamer 12.Based on this data, an image of the subsurface is generated.

Alternatively, ocean bottom cables (OBC) or ocean bottom nodes (OBN) andocean bottom seismometers (OBS) may be used to record the seismic data.FIG. 2 shows an OBC 30 that includes plural receivers 32 distributed onthe ocean bottom 20, which may be connected to each other (or may beindependent OBN/OBS) with a cable 33 that may also be connected to adata collection unit 34. Various means (e.g., underwater vehicle) may beused to retrieve the seismic data from the data collection unit 34 andbring it on the vessel 10 for processing.

When these marine-based survey techniques are used to monitor aproducing reservoir the location of the streamers or nodes may varybetween vintages. Variability in the two seismic datasets is alsointroduced when a first dataset is collected using one technique and thesecond dataset is collected using the other technique. As 4D processingdetermines changes in Earth properties by evaluating differences inseismic data acquired at different times but processed together, thesuccess of 4D processing depends on how well differences in acquisitionmethods and geometries are handled during data processing and imaging.

If these differences are accurately compensated, changes in thesubsurface that are related to fluid production can be identified byareas of significant difference between baseline and monitor imagesafter migration. Failure to compensate for acquisition differences leadsto the creation of 4D noise, which is an appreciable difference ofbaseline and monitor migrated images not attributable to reservoirproduction. Differences in both information content and wavefieldsampling lead to this generation of 4D noise. Therefore, it is desirableto address acquisition differences through more accurate methods of dataprocessing.

Accurate selection of subsets of seismic traces in the base and monitorseismic surveys reduces the level of 4D noise in the migrated images bychoosing subsets of data that migrate to give the same image, free ofthe effects of acquisition differences and other sources of 4D noise,and giving a 4D image with a faithful representation of petroleumproduction activity. Conventional methods utilize 4D-binning to selecttraces from the base and monitor surveys for further processing based ona set of criteria that assess their degree of similarity. Theseconventional methods evaluate a set of similarity criteria in the datadomain, i.e., before migration, and work well when the base and monitorsurveys have similar acquisition geometry, for example, a towed-streamerbase survey and a towed-streamer monitor survey acquired in similarpositions but at different times. However, when the base and monitorsurveys have different acquisition geometries, for example, atowed-streamer base survey and a sparse OBN monitor survey, the surfaceor data domain trace attributes used to measure similarity in the4D-binning process are not a good proxy for similarity of the traces inthe seismic datasets or for the wavefield sampling in the seismicdatasets. Furthermore, the evaluation of similarity using surface ordata domain trace attributes cannot accurately measure similarity ofinformation content, since the grouping of traces by surface attributesdoes not allow the comparison of similar parts of the seismic wavefield.

Therefore, the need exists for improved methods and systems forimproving the similarity between two seismic datasets even given changesin acquisition geometries, e.g., towed-streamer and ocean-bottom data.These improved systems and methods would be applicable to 4D processingfor seismic datasets associated with multiple seismic survey vintagesconducted at different times.

SUMMARY

Exemplary embodiments are directed to methods and systems that providefor the processing of two seismic datasets to improve the similarity ofthese seismic datasets. These seismic datasets can be associated withtwo separate seismic survey vintages, which are acquired at differenttimes, usually before, i.e., the baseline survey, and after, i.e., themonitor survey, a period of fluid production from a petroleum reservoir.This multi-vintage processing is time-lapse or 4D processing of the twoseismic datasets. Through 4D processing, changes in Earth properties ofthe subsurface being surveyed are identified by evaluating differencesin the co-processed seismic datasets.

Processing of the two seismic datasets includes migrating the seismicdata to the image domain from the data domain. In addition to migrationto the image domain in general, the seismic datasets are migrated todip-angle image domain, which is a sub-class of the more general imagedomain. In the dip-angle image domain, components of the migrated imageare separated to a set of different partial images based on thereflector dip being imaged. Therefore, each partial image in thedip-angle image domain is associated with a reflector dip or dip angle.The number of partial images and the associated dip angles are selectedusing techniques including anti-aliasing techniques where the dip anglesare associated with frequency bands and dip filtering where only asubset of the entire available dip angle range is used.

Each partial image is generated for each seismic dataset. The result isa set or pair of partial images for each dip angle. These pairs ofpartial images are then compared to identify similarities and also tocalculate a decimation or weighting function that can be applied to eachpartial image in each pair of partial images to increase the similaritybetween the pairs of partial images. This is conducted in the dip angleimage domain, and the weighting function can be an analogue or binary(0,1) system that decimates or reduces those parts of the partial imagesand seismic datasets. Having increased the similarity between pairs ofweighted partial images, these partial images with improved similarityare then recombined to generate an image of the subsurface havingreduced noise. This subsurface image can then be used to analyze changesin the subsurface attributable to reservoir production.

According to an exemplary embodiment, there is a method for increasingsimilarity between a base seismic survey and a monitor seismic survey ofa same surveyed subsurface during a 4-dimensional (4D) project. Themethod includes receiving first seismic data associated with the baseseismic survey; receiving second seismic data associated with themonitor seismic survey, wherein the monitor seismic survey is performedlater in time than the base seismic survey; migrating the first andsecond seismic data to an image domain; and calculating, with aprocessor, a set of decimating weights based on the migrated first andsecond seismic data in the image domain, to maximize a similaritybetween the first seismic data and the second seismic data.

According to another exemplary embodiment, there is a computing devicefor increasing similarity between a base seismic survey and a monitorseismic survey of a same surveyed subsurface during a 4-dimensional (4D)project. The computing device includes an interface configured toreceive first seismic data associated with the base seismic survey andsecond seismic data associated with the monitor seismic survey, whereinthe monitor seismic survey is performed later in time than the baseseismic survey; and a processor connected to the interface. Theprocessor is configured to migrate the first and second seismic data toan image domain, and calculate a set of decimating weights based on themigrated first and second seismic data in the image domain, to maximizea similarity between the first seismic data and the second seismic data.

An embodiment is directed to a method for increasing similarity betweena first seismic dataset and a second seismic dataset from at least oneseismic survey of a subsurface. The first and second seismic datasetsare obtained. In one embodiment, the first seismic dataset includes aplurality of first seismic traces obtained from a base seismic survey,and the second seismic dataset includes a plurality of second seismictraces obtained from a monitor seismic survey conducted after the baseseismic survey.

The first seismic dataset and second seismic dataset are migrated to adip angle image domain. In one embodiment, a subset of the first seismictraces and a subset of the second seismic traces are selected, and thesubset of first seismic traces and the subset of second seismic tracesare migrated. The migrated first seismic dataset and the migrated secondseismic dataset are used in the dip angle image domain to calculate,with a processor, a set of decimating weights to be applied to the firstseismic dataset and the second seismic dataset to maximize a similaritybetween the first seismic dataset and the second seismic dataset. In oneembodiment, the set of decimating weights is calculated to be applied tothe first seismic dataset and the second seismic dataset in the dipangle image domain. The decimated weights are applied to the firstseismic dataset and the second seismic dataset, and an image of thesubsurface is generated using the first seismic dataset and the secondseismic dataset following application of the decimated weights.

In one embodiment, the first seismic dataset is migrated to a pluralityof first seismic dataset dip angle partial images, and the secondseismic dataset is migrated to a plurality of second seismic dataset dipangle partial images. Each first seismic dataset and second seismicdataset dip angle partial image is associated with one of a plurality ofdip angles. A measure of similarity between a first seismic dataset dipangle partial image and a second seismic dataset dip angle partial imageis determined for each pair of dip angle partial images associated witha common one of the plurality of dip angles. Preferably, the measure ofsimilarity is a point by point measure of similarity.

A set of decimating weights is calculated for the first seismic datasetdip angle partial image and the second seismic dataset dip angle partialimage in each pair of dip angle partial images. Each set of decimatingweights when multiplied to the first seismic dataset dip angle partialimage and the second seismic dataset dip angle partial image in a givenpair of dip angle partial images increases a similarity of the firstseismic dataset dip angle partial image and the second seismic datasetdip angle partial image in that given pair of dip angle partial images.Each set of decimating weights is multiplied to the first seismicdataset dip angle partial image and the second seismic dataset dip anglepartial image in the pair of dip angle partial images associated withthat set of decimating weights. The first seismic dataset dip anglepartial image and the second seismic dataset dip angle partial image ineach pair of dip angle partial images are combined followingmultiplication to produce an image of the subsurface.

In one embodiment, the plurality of dip angles are selected byidentifying a plurality of frequency bands in the first and secondseismic datasets and associating a dip angle with each one of theplurality of frequency bands. The lower frequencies are associated withlarger dip angles, and the higher frequencies are associated withsmaller dip angles. In one embodiment, the plurality of dip angles areselected by identifying a smallest dip angle, which is greater thanzero, identifying a largest dip angle, which is less than a maximum dipangle in the subsurface and selecting the plurality of dip anglesbetween the smallest dip angle and the largest dip angle.

An embodiment is directed to a computing device increasing similaritybetween a first seismic dataset and a second seismic dataset from atleast one seismic survey of a subsurface. The computing device includesa database containing the first seismic dataset and the second seismicdataset. The computing device also includes a processor in communicationwith the database and configured to migrate the first seismic datasetand second seismic dataset to a dip angle image domain and use themigrated first seismic dataset and the migrated second seismic datasetin the dip angle image domain to calculate, with a processor, a set ofdecimating weights to be applied to the first seismic dataset and thesecond seismic dataset to maximize a similarity between the firstseismic dataset and the second seismic dataset.

In one embodiment, the processor is further configured to migrate thefirst seismic dataset to a plurality of first seismic dataset dip anglepartial images and the second seismic dataset to a plurality of secondseismic dataset dip angle partial images. Each first seismic dataset andsecond seismic dataset dip angle partial image is associated with one ofa plurality of dip angles. In one embodiment, the processor is furtherconfigured to determine a measure of similarity between a first seismicdataset dip angle partial image and a second seismic dataset dip anglepartial image for each pair of dip angle partial images associated witha common one of the plurality of dip angles. The measure of similaritycan be a point by point measure of similarity.

In one embodiment, the processor is further configured to calculate aset of decimating weights for the first seismic dataset dip anglepartial image and the second seismic dataset dip angle partial image ineach pair of dip angle partial images. Each set of decimating weightswhen multiplied to the first seismic dataset dip angle partial image andthe second seismic dataset dip angle partial image in a given pair ofdip angle partial images increases a similarity of the first seismicdataset dip angle partial image and the second seismic dataset dip anglepartial image in that given pair of dip angle partial images.

In one embodiment, the processor is further configured to multiply eachset of decimating weights to the first seismic dataset dip angle partialimage and the second seismic dataset dip angle partial image in the pairof dip angle partial images associated with that set of decimatingweights and combine the first seismic dataset dip angle partial imageand the second seismic dataset dip angle partial image in each pair ofdip angle partial images following multiplication to produce an image ofthe subsurface.

According to still another embodiment, there is a non-transitorycomputer readable medium including computer executable instructions,wherein the instructions, when executed by a computer, implement themethods discussed above.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate one or more embodiments and,together with the description, explain these embodiments. In thedrawings:

FIG. 1 is a schematic diagram of a conventional seismic data acquisitionsystem having plural seismic receivers on streamers;

FIG. 2 is a schematic diagram of a conventional seismic data acquisitionsystem having plural seismic receivers located at the ocean bottom;

FIG. 3 is a flowchart illustrating a novel method for 4D-binningaccording to an exemplary embodiment;

FIG. 4 is a flowchart illustrating a more detailed method for 4D-binningaccording to an exemplary embodiment;

FIG. 5 is a flowchart illustrating a step of the method for 4D-binningof FIG. 4 according to an exemplary embodiment;

FIG. 6 is a flowchart illustrating a method for 4D-binning according toan exemplary embodiment;

FIG. 7 is a flowchart illustrating another method for 4D-binningaccording to an exemplary embodiment; and

FIG. 8 is a schematic diagram of a computing device for implementing theabove methods.

DETAILED DESCRIPTION

The following description of the exemplary embodiments refers to theaccompanying drawings. The same reference numbers in different drawingsidentify the same or similar elements. The following detaileddescription does not limit the invention. Instead, the scope of theinvention is defined by the appended claims. The following embodimentsare discussed, for simplicity, with regard to seismic data collectedduring a base survey and a monitor survey, wherein the base survey wasconducted with streamers and the monitor survey was conducted with oceanbottom nodes (OBNs). However, the embodiments to be discussed next arenot limited to these kinds of surveys. For example, the novelembodiments may be applied to a base survey conducted with OBNs and amonitor survey conducted with streamers or the baseline and monitorsurveys may be different wavefields such as an upgoing wavefield and adowngoing wavefield. More generally, the novel embodiments aresuccessful for base and monitor seismic surveys that may have differentinformation content and/or wavefield sampling.

Reference throughout the specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with an embodiment is included in at least oneembodiment of the subject matter disclosed. Thus, the appearance of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout the specification is not necessarily referring to the sameembodiment. Further, the particular features, structures orcharacteristics may be combined in any suitable manner in one or moreembodiments.

Exemplary embodiments are directed to methods and systems for matchingtwo seismic datasets, i.e., a first seismic dataset and a second seismicdataset. In one embodiment, these seismic datasets represent twoseparate parts of a common seismic survey acquisition. Alternatively,these seismic datasets are associated with two separate vintages ofseismic surveys, a base survey conducted at a first time and a monitorsurvey conducted at a second later time. The two seismic datasets areprocessed and matched using four-dimensional (4D) processing. Thisprocessing improves or increases the similarities between the twoseismic datasets, reducing 4D noise and improving the use of these twoseismic datasets to image changes over time in subsurface structuressuch as reservoirs that are attributable to reservoir productiontechniques.

Referring initially to FIG. 3, in one embodiment a first step of using adata domain decimation strategy 300 is linked to a second step of usingan image domain similarity measure 302. This combination of (i) datadomain decimation and (ii) image domain similarity measure is animprovement over the conventional use of data domain attributes in4D-binning. Following decimation in the data domain and similaritydetermination in the image domain, traditional processing is applied ina third step 304. This traditional processing includes migration on thedata binned according to the first and second steps 300 and 302. In thefourth step, a final image of the surveyed subsurface is generated 306.

As discussed above, measuring similarity between two seismic datasetsbased on data domain attributes cannot accurately match seismic tracesobtained using different acquisition geometries. However, a moreaccurate match is obtained by evaluating the need for decimation of twoseismic datasets in the common migrated-image domain and in particularin the dip angle image domain. This image domain similarity comparisonand decimation determination is applicable in the 4D processing of bothland and marine seismic acquisition systems, including towed-streamerand ocean-bottom node systems, in which the acquisition geometries vary.

In one embodiment, the subsurface image (I) of each one of a firstseismic dataset and a second seismic dataset are used to calculate a setof decimating weights (w) that can be multiplied to the first and secondseismic datasets to reduce these input datasets (d) to a common level ofinformation and wavefield sampling. In one embodiment, the decimatingweights are determined by requiring the first and second seismicdatasets following decimation to be equal. This equality can bedetermined in a least-squares sense or any other equivalent minimizingnorm when using sections of the input first and second datasets known tocorrespond to stationary parts of Earth. Alternatively, estimation ofthe decimation weights is conditioned by requiring the set of decimatingweights to have a minimum gradient, which promotes similarity of weightsfor adjacent seismic traces, or to have a minimum total magnitude on thecomplementary set of weights, which maximizes the data that survive theprocess and prevents the trivial solution of all data being weighted tozero. As used herein, a complementary weight, {tilde over (w)}, isdefined as {tilde over (w)}=1−w, for weight w=[0,1]. In one embodiment,the decimating weights are determined as a function that varies on asample by sample, or trace by trace, basis. In another embodiment, noexplicit grouping of seismic traces is used in the image domain4D-binning method.

In accordance with one exemplary embodiment, a matrix A is defined thatcontains coefficients of a diffraction-stack migration, a Kirchhoffmigration or any known migration in its rows. Suitable types ofmigrations are known and available in the art. The defined matrix, A,which can also be referred to as a transform, maps the seismic datasetsd onto a migrated image I_(a). The transform A models a propagation ofthe wavefields in the subsurface via ray-tracing through an arbitrarysubsurface model. Coefficients in A's rows are placed such thatmultiplication of each row of A with the data vector d achieves theKirchhoff integral for a particular image point. The image point isrepresented by the row of the image vector I_(a). Subscript a on theimage vector indicates an evaluation of the image at a specific ensembleof points in the image domain that do not have to be regularlydistributed. If subscript b is used to indicate the baseline survey,migration of this dataset can be expressed as:

I _(a|b) =A _(b) d _(b)  , (1)

where the data vector d_(b) is of dimensions (m×1), the image vectorI_(a|b) is of dimensions (p×1), and the migration matrix A_(b) is ofdimensions (p×m). Similarly, for the monitor (subscript m) the migrationof the corresponding dataset can be written as:

I _(a|m) =A _(m) d _(m)  , (2)

where the data vector d_(m) is now (n×1), the image vector I_(a|m) isstill (p×1), and the migration matrix A_(m) is (p×n). In both cases, theimage is evaluated at the same position ensemble a in the image domain.

In one embodiment, a decimation strategy is formulated that makes use ofmigration engine A to evaluate the similarity of two seismic datasets ortwo vintages in the image domain. Use of a common image domain allowstwo seismic datasets obtained using different acquisition geometries,e.g., towed-streamer and ocean-bottom data, to be accurately comparedwithout regard to these differences in acquisition geometry or any otherissue that makes data domain comparisons difficult. Therefore, exemplaryembodiments overcome the difficulties associated with conventionalmethods.

Considering a stationary part of the seismic dataset, which should bereferred to in the following as a “training” dataset, the training setcould be chosen in a time window known to correspond with data above theproducing reservoir. A characteristic of the training dataset is thatthere is no 4D signal in it, so the migrated images should be equal ifthe decimating weights are correct.

A set of decimating weights w_(b) and w_(m) are introduced, and thesedecimating weights should satisfy the following equations:

I _(a|b) =A _(b) w _(b) d _(b)   (3)

and

I _(a|m) =A _(m) w _(m) d _(m)  , (4)

where the baseline weights w_(b) form a diagonal matrix of dimensions(m×m), and the monitor weights w_(m) form a diagonal matrix ofdimensions (n×n). Because these weights operate on the training dataset,then, if the decimating weights are correct, the following equalityholds I_(a|b)=I_(a|m).

Thus, an object of the method is to find, by optimization, the set ofdecimating weights that achieves this equality. To achieve this, it ispossible to concatenate (i) the data vectors d_(b) and d_(m) into asingle vector of length (m+n) and (ii) the weights matrices w_(b) andw_(m) into a diagonal matrix of dimensions ([m+n]×[m+n]). Then, inflatethe number of columns and zero-pad the migration matrices A_(b) andA_(m) such that the finite coefficients of the baseline migration areplaced in the first p rows and m columns of A′_(b), which is (p×[m+n]),and the finite coefficients of the monitor migration are placed in thefirst p rows and last n columns of A′_(m), which is also (p×[m+n]).Hence, equations (3) and (4) may be re-written as:

I _(a|b) =A′ _(b) wd   (5)

and

I _(a|m) =A′ _(m) wd  , (6)

where A′_(b)=[A_(b)|O_(b)] for a zero-matrix 0_(b) of dimensions (p×n),A′_(m)=[0_(m)|A_(m)] for a zero-matrix 0_(m) of dimensions (p×m),

${w = \begin{bmatrix}w_{b} & 0_{m}^{\prime} \\0_{b}^{\prime} & w_{m}\end{bmatrix}},$

for zero-matrices 0′_(b) of dimensions (n×m) and 0′_(m) of dimensions(m×n), d=[d_(b) ^(T)|d_(m) ^(T)]^(T), and operation T means transpose.

Based on equations (5) and (6), a residuals vector r may be formed asfollows:

r=I _(a|b) −I _(a|m) =A′ _(b) wd−A′ _(m) wd=(A′ _(b) −A′ _(m))wd.   (7)

The set of decimating weights may be estimated by minimizing r^(T)r, orby use of some other norm (such as an L1-norm) to minimize theresiduals.

The set of decimating weights can be specified to take the valuesw=diag(w_(ii)=0,1) for i=1, . . . , m+n. The term diag(w_(ii)=0,1) meansthat the matrix is diagonal, so it has values only on the main diagonaland zeros everywhere else. The values that are on the main diagonal canbe either 0 or 1, depending on what produces the most similar image. Bya weight of 0, that part of the data is not included in the migration.By a weight of 1, the data is included. Other values are also possible,but would make the problem less well conditioned. Furthermore, theweights may be allowed to vary either sample by sample, or to takeblocks of values that represent the decimation of entire traces at atime.

The problem of estimating decimating weights by minimizing the residualsvector can be further conditioned by placing various regularizing termson the weights. For example, the trivial solution of matching twovintages by making all weights zero can be avoided by requiring the setof complementary weights {tilde over (w)}_(ii) to be {tilde over(w)}_(ii)=1,0 for w_(ii)=0,1, and to have minimum total magnitude {tildeover (w)}^(T){tilde over (w)}. Similarly, the weights may be required tobe flat (a minimum of the gradient of the weights), which promotesblocks of weights with similar values under the premise that adjacenttraces are likely to hold similar information content. Finally, acondition that requires even trace density may be added to promotequality of the 3D migrated image after the image domain 4D-binning.

A few practical details associated with the novel method are nowdiscussed. In one application, the migration matrices are large, beingof dimensions (p×[m+n]) for p image points and m+n datapoints in thecombined baseline and monitor. Furthermore, the migration matrices arenon-sparse because they contain coefficients distributed over the datadomain that includes Greens functions of the image points. Thus, thematrices may be too large to produce a solution of the entire datasetsin one pass. Nevertheless, by dividing the data into overlappingspatio-temporal blocks, a set of weights may be obtained for each block,and then it is possible to separately decimate the data prior to a finalmigration.

Before the final migration takes place, further normalizing the data inthe overlap zones by their duplication number allows the final imagesfrom each block to be summed together. By dividing the data intooverlapping spatio-temporal blocks, one trace may be duplicated in theoverlap of q blocks. The duplication number for this trace is then q.The process may thus be parallelized in both the image domain (choosesmall p blocks) and in the data domain (choose small m, n blocks),although one of the domains needs to be large enough to contain themigration operator at a given aperture.

Having determined the set of decimating weights, these can be applied tothe data prior to a final migration using an imaging algorithm externalto the subsurface 4D-binning process.

Thus, the image domain 4D-binning method improves the 4D match betweentwo surveys with very different acquisition geometries, in particularfor towed-streamer and ocean-bottom node data.

Exemplary embodiments link data domain decimation to similarity measuresfor the common migrated-image domain. In one embodiment, explicitgrouping of traces by spatial bin and offset or angle class is notrequired. In another embodiment, a similarity measure based on surfaceor data domain trace attributes is not required. Consequently, themethod advantageously improves the accuracy of 4D-binning when the inputseismic datasets have different acquisition geometries.

Referring to FIG. 4, an exemplary embodiment of a 4D-binning algorithmis illustrated. In a first step 400, 4D seismic data are received. The4D seismic data include two seismic datasets from at least two different3D seismic surveys. In one application, one seismic dataset has beencollected using streamers while a second seismic dataset has beencollected using OBNs. The 4D seismic data, i.e., the first or baseseismic dataset and the second or monitor dataset, are split 402 into aone or more overlapping spatio-temporal blocks. One block from eachseismic dataset is concatenated into a working data vector d of lengthm+n for m datapoints in the baseline block and n data points in themonitor block 404. Thus, a plurality of data vectors is formed as aresult of this process. Next, each data vector is processed separately.Considering the single data vector d, an ensemble of image points isdefined for the data vector 406. The ensemble of image points(associated with I_(a)) is defined so that the data vector d is mappedto it by migration A. The image points may or may not be regularlydistributed in the image domain. The ensemble of image points may be thesame during the entire process or may be chosen for each data block.

Next, a pre-defined velocity model is received 408. The velocity modeldescribes the propagation speed of sound in water and in the subsurface,and this velocity model may be obtained in any suitable way known andavailable to those skilled in the art. Based on the velocity model, thealgorithm calculates (e.g., by ray tracing) Green's functions 410 ofseismic waves that propagate from each image point in the ensemble tothe position of each source and each receiver in the data vector. Thesource and receiver Green's functions for each image point in theensemble are combined 412 to define the coefficients of a Kirchhoffintegral in a corresponding row of migration matrices A′_(b) and A′_(m).If another migration method is used, then a corresponding quantity iscalculated and not the Kirchhoff integral. Having the migration matricesA′_(b) and A′_(m), a net transform matrix A=A′_(b)−A′_(m) is formed 414.Next, based on the net transform matrix A, the weights w are formed 416.

Referring to FIG. 5, an embodiment of calculating the weights isillustrated. Overall for calculating weights 416, a vector of weightsw=diag[w_(ii)] is formed for i=1, . . . , m+n. The diagonal elements ofthe vector are set to be one, i.e., w_(ii)=1 416 a. Then, thecorresponding vector of complementary weights {tilde over (w)} is formed416 b so that {tilde over (w)}=[{tilde over (w)}_(ii)=1,0] whenw=[w_(ii)=0,1] and i=1, . . . , m+n. Note that the notation {tilde over(w)}_(ii)=1,0 means a diagonal element of the vector w takes a value oneor zero, and all other elements of the vector take a zero value. Theresiduals vector r is calculated 416 c based on formula r=Awd. A costfunction E is defined and then evaluated 416 d based on formulaE=r^(T)r+{tilde over (w)}^(T){tilde over (w)}. Note that additionalterms may be added to the cost function to promote, for example,flatness in the spatial trace density, or to improve the quality of the3D migrated images.

Cost function E is minimized with respect to the set of weights 416 eusing various mathematical methods. For example, minimization of thecost function may be achieved using the method of conjugate gradientsapplied to the vector of weights w. In one embodiment, the algorithmloops back from sub-step 416 f to sub-step 416 b until the cost functionis minimized. Note that the set of weights may be varied at each stepand thus, complementary weights need to be re-calculated in each step.Once the cost function has been minimized, the vector of weights thatminimizes the cost function for the given data d is obtained. Then, thealgorithm returns to step 406 to address the next vector of data untilall data is processed.

Returning to FIG. 4, the vector of weights w for all data d ismultiplied 418 by the vector of data d and then the weighted data vectoris split back 420 into the spatio-temporal blocks of step 402. Followingthis, data are normalized in the overlap zones of the blocks by theirduplication number 422. After one or more processing steps, the finalimage of the surveyed subsurface is generated 424.

Referring to FIG. 6, an exemplary embodiment of a method for increasinga similarity between a first seismic dataset, for example from baseseismic survey, and a second seismic dataset, for example from a monitorseismic survey of a same surveyed subsurface during a 4-dimensional (4D)project is illustrated. A first seismic dataset associated with the baseseismic survey is obtained or received 600, and a second seismic datasetassociated with the monitor seismic survey is also received 602. Themonitor seismic survey is performed later in time than the base seismicsurvey. The first and second seismic datasets are migrated an imagedomain 604. Using a processor, a vector of decimating weights iscalculated based on the migrated first and second seismic datasets inthe image domain to maximize a similarity between the first seismicdataset and the second seismic dataset 606.

Referring to FIG. 7, another exemplary embodiment of a method forincreasing the similarity between a first seismic dataset and a secondseismic dataset from at least one seismic survey of a subsurface 700 isillustrated. In accordance with this embodiment, the seismic datasetsare migrated to a dip-angle image domain, which is defined as asub-class of the more general image domain. In the dip angle imagedomain, components of the migrated image are separated to a set ofdifferent partial images based on the reflector dip being imaged.

The first seismic dataset is obtained 702, for example from a databaseor from a seismic acquisition survey. The first seismic dataset includesa plurality of first seismic traces obtained, for example, from a baseseismic survey. The second seismic dataset is acquired 704, for examplefrom a database or from a seismic acquisition survey. The second seismicdataset includes a plurality of second seismic traces obtained, forexample, from a monitor seismic survey conducted after the base seismicsurvey.

Each one of the first and second seismic datasets are migrated in thedip angle domain. This can be achieved by migrating the seismic datasetsto multiple partial images, where each image is associated with a givendip angle or range of dip angle, for example, for dips located withinthe subsurface. Therefore, in one embodiment, a plurality of dip anglesis selected 706. In general, high and very high dips are necessary toimage punctual structure, edges or diffractors. On the contrary, in caseof more or less flat and horizontal structures, using higher dipsresults in migration noise increase. Therefore, the dip selection for athree-dimensional (3D) subsurface is applied to 4D migration processing.

In accordance with one embodiment, anti-aliasing is utilized to optimizedip selection. Therefore, a plurality of frequency bands is selected oridentified in the first and second seismic datasets, and a dip angle isassociated with each one of the plurality of frequency bands. The lowerfrequencies are associated with larger dip angles, and higherfrequencies are associated with smaller dip angles. In 3D imaging,aliasing increases with the frequency and with the migration dip. Onetechnique for compensating for the effects of aliasing separates theinitial traces into a plurality, N, of bandwidths and migrates the Ndifferent bandwidths with different dips. The N per-bandwidths migratedsections are then added together.

Adapting the anti-aliasing technique to 4D focuses the 4D seismic data.This focusing is increased by using higher frequencies and higher dips.Regarding dip, the choice is performed in relation to the 4D events atdepth within the subsurface. For example, if the subsurface medium is alayered and horizontal medium, optimal dips for 3D imaging can be low,e.g., 10 to 15 degrees. In 4D, an event occurring in the reservoir canbe punctual or sharply terminated in location, for example, when steaminjection starts. Therefore, these events in 4D are better imaged withhigher dips, e.g., up to 40 degrees or more. In many cases, 4D eventscan present dips significantly different compared to 3D structures.Therefore, exemplary embodiments, when performing anti-aliasing in dipangle selection choose dips that are not optimized for 3D imaging butfor 4D attributes. As illustrated in Table 1, lower dip angles areassociated with higher frequencies in order to benefit from more highfrequencies (anti-alias), and higher dip angles are associated withlower frequencies to better focus the 4D event.

TABLE 1 Anti-Aliased Migration Adapted For 4D Frequency sub-band [10 50]Hz [50 110] Hz [110 200] Hz Optimal dip 35° 23° 12°

In one embodiment of using the anti-aliasing techniques in 4D, theplurality of frequency bands are selected in the seismic datasets, and adesired or optimal dip angle is selected for each one of the frequencybands. Each one of the first and second seismic datasets are thenseparately migrated into the partial dip angle images of the dip anglesin accordance with these frequency bands. The result is a plurality ofpartial images for each seismic data set. These plurality of partialimages can then be used in additional 4D processing with or withoutsumming

Another embodiment of dip angle selection utilizes dip angle filtering.The optimal choice of dip angle can be done using dip filtering. In thisembodiment, a smallest dip angle is identified and is greater than zero,δ₁>0, and a largest dip angle, δ₂, is identified that is less than amaximum dip angle in the subsurface. In the plurality of dip angles, theselected dip angle are located between the smallest dip angle and thelargest dip angle, δ₂>δ₁. While migration with dip ranging from δ₁>0 toδ₂>δ₁ may not produce a real 3D image, 4D seismic data can largelypresent a certain dip and can be better isolated using dip filtering.Therefore, dip filtering brings additional information to detectreservoir activity.

In this embodiment, a plurality of dips ranges is selected, each one ofthe first and second seismic datasets are migrated into each one of theplurality of ranges. This results in a plurality of dip angle imagedomain partial images for each seismic dataset. Each partial image isassociated with a given dip angle or range of dip angles. These partialdip angle images are then used for additional 4D processing andsubsurface imaging. This embodiment can be used to compute attributes onmigration preformed with dip sub-ranges or to optimize dip at depth ofinterest before any type migration or processing using migration.

Having selected the dip angles for migration, the first seismic datasetand second seismic dataset are migrated to a dip angle image domain 708.Any suitable method for migrating seismic data known and available inthe art can be used. As the first and second seismic datasets contain aplurality of seismic traces, a subset of the first seismic traces and asubset of the second seismic traces are selected. The subset of firstseismic traces and the subset of second seismic traces are thenmigrated. In one embodiment, different subsets of the seismic traces areiteratively selected and processed for similarity.

In one embodiment, the first seismic dataset is migrated to a pluralityof first seismic dataset dip angle partial images, and the secondseismic dataset is migrated to a plurality of second seismic dataset dipangle partial images. Each first seismic dataset and second seismicdataset dip angle partial image is associated with one of a plurality ofselected dip angles. In one embodiment, this separation into partialdip-angle images is achieved using the slowness vectors of thesource-side and receiver-side rays propagated from the acquisitionsurfaces to the image point. The sum of these slowness vectors is theillumination slowness vector that is normal to the reflector beingimaged as described, for example, in Audebert, F. et al., “Migration InThe Angle Domain, An Inside View”, EAGE 65th Conference & Exhibition(2003). Using the illumination slowness vector it is possible toseparate the image into component parts based on the geological dip ofthe reflectors, Klokov. A. and Fomel. S., “Selecting An Optimal ApertureIn Kirchhoff Migration Using Dip-Angle Gathers”, Geophysics, v 78,(2013) and Resher, M. and Landa, E., “Post-Stack Velocity Analysis InThe Dip-Angle Domain Using Diffractions”, Geophysical Prospecting, v57,(2009).

The migrated first seismic dataset and the migrated second seismicdataset in the dip angle image domain are used to calculate a set ofdecimating weights 710. These decimating weights are to be applied tothe first seismic dataset and the second seismic dataset to maximize asimilarity between the first seismic dataset and the second seismicdataset. In particular, the set of decimating weights is calculated tobe applied to the first seismic dataset and the second seismic datasetin the dip angle image domain, as opposed to the data domain. The set ofdecimating weights can be a set of analogue weights or a set of binaryweights, (0,1), that are applied or multiplied to the seismic datasetsto suppress or eliminate those portions of the seismic datasets that donot contribute to the similarity of those datasets in the dip angleimage domain. For example, the weights are applied to the individualseismic traces in each of the seismic datasets.

Similarity is determined between pairs of dip angle partial imagesassociated with a common dip angle. One of the dip angle partial imagesin the pair of images is associated with the first seismic dataset, andthe other dip angle partial image in the pair of images is associatedwith the second seismic dataset. In one embodiment, a measure ofsimilarity between a first seismic dataset dip angle partial image and asecond seismic dataset dip angle partial image is determined for eachpair of dip angle partial images associated with a common one of theplurality of dip angles. Suitable measures of similarity include, butare not limited to, a point by point measure of similarity. In oneembodiment, a set of decimating weights for the first seismic datasetdip angle partial image and the second seismic dataset dip angle partialimage in each pair of dip angle partial images is calculated. Each setof decimating weights when multiplied to the first seismic dataset dipangle partial image and the second seismic dataset dip angle partialimage in a given pair of dip angle partial images increases a similarityof the first seismic dataset dip angle partial image and the secondseismic dataset dip angle partial image in that given pair of dip anglepartial images.

Having generated the set of decimating weights, these decimated weightsare applied to the first seismic dataset and the second seismic dataset712. In one embodiment, each set of decimating weights is multiplied tothe first seismic dataset dip angle partial image and the second seismicdataset dip angle partial image in the pair of dip angle partial imagesassociated with that set of decimating weights. An image of thesubsurface is generated using the first seismic dataset and the secondseismic dataset following application of the decimated weights 714. Inone embodiment, the first seismic dataset dip angle partial image andthe second seismic dataset dip angle partial image in each pair of dipangle partial images following multiplication are combined to produce animage of the subsurface.

The produced image can then be output, i.e., displayed to a user, orsaved, and can be used to analyze past reservoir production and guidefuture reservoir production. In accordance with exemplary embodiments,the dip angle domain is included in the scope of the claims as asub-class of image domain, and the set of decimating weights are appliedin the image domain and not in the data domain. Dip angle decompositionensures the first and second seismic datasets are transformed to acommon domain, i.e., the depth domain, in a form suitable forhigh-quality similarity-enhancing processing with dip decomposition.This is particularly useful for mixed-geometry 4D surveys, such as atowed-streamer baseline and an OBN monitor survey, which do nototherwise provide a common domain in which to conductsimilarity-enhancing processes with these datasets.

In addition to the increased similarity of baseline and monitor imageson reflecting interfaces, the use of dip-angle images for similarityprocessing allows the detection and removal of coherent noise such asremnant multiple, seismic interference, backscattered energy and othersources of noise resent in either one of the baseline or monitor.

Referring to FIG. 8, an embodiment of a representative computing device800 capable of carrying out operations in accordance with the exemplaryembodiments is illustrated. Hardware, firmware, software or acombination thereof may be used to perform the various steps andoperations described herein.

The exemplary computer device 800 suitable for performing the activitiesdescribed in the exemplary embodiments may include server 801. Such aserver 801 may include a central processor unit (CPU) 802 or processorcoupled to and in communication with a database such as s random accessmemory (RAM) 804 and to a read-only memory (ROM) 806. ROM 806 may alsobe other types of storage media to store programs, such as programmableROM (PROM), erasable PROM (EPROM), etc. Processor 802 may communicatewith other internal and external components through input/output (I/O)circuitry 808 and bussing 810 to provide control signals and the like.Processor 802 carries out a variety of functions as are known in theart, as dictated by software and/or firmware instructions.

In one embodiment, the databased stores the first seismic dataset andthe second seismic dataset. The processor, which is in communicationwith the database is configured to migrate the first seismic dataset andsecond seismic dataset to a dip angle image domain and to use themigrated first seismic dataset and the migrated second seismic datasetin the dip angle image domain to calculate a set of decimating weightsto be applied to the first seismic dataset and the second seismicdataset to maximize a similarity between the first seismic dataset andthe second seismic dataset.

In one embodiment, the processor is further configured to migrate thefirst seismic dataset to a plurality of first seismic dataset dip anglepartial images and the second seismic dataset to a plurality of secondseismic dataset dip angle partial images. Each first seismic dataset andsecond seismic dataset dip angle partial image is associated with one ofa plurality of dip angles. In one embodiment, the processor is furtherconfigured to determine a measure of similarity between a first seismicdataset dip angle partial image and a second seismic dataset dip anglepartial image for each pair of dip angle partial images associated witha common one of the plurality of dip angles. Suitable measures ofsimilarity include, but are not limited to, a point by point measure ofsimilarity. In one embodiment, the processor is further configured tocalculate a set of decimating weights for the first seismic dataset dipangle partial image and the second seismic dataset dip angle partialimage in each pair of dip angle partial images. Each set of decimatingweights when multiplied to the first seismic dataset dip angle partialimage and the second seismic dataset dip angle partial image in a givenpair of dip angle partial images increases a similarity of the firstseismic dataset dip angle partial image and the second seismic datasetdip angle partial image in that given pair of dip angle partial images.

In one embodiment, the processor is further configured to multiply eachset of decimating weights to the first seismic dataset dip angle partialimage and the second seismic dataset dip angle partial image in the pairof dip angle partial images associated with that set of decimatingweights and to combine the first seismic dataset dip angle partial imageand the second seismic dataset dip angle partial image in each pair ofdip angle partial images following multiplication to produce an image ofthe subsurface.

Server 801 may also include one or more data storage devices, includinghard disk drives 812, CD-ROM drives 814, and other hardware capable ofreading and/or storing information such as a DVD, etc. In oneembodiment, software for carrying out the above-discussed steps may bestored and distributed on a CD-ROM or DVD 816, removable media 818 orother forms of media capable of portably storing information. Thesestorage media may be inserted into, and read by, devices such as theCD-ROM drive 814, the drive 812, etc. Server 801 may be coupled to adisplay 820, which may be any type of known display or presentationscreen, such as LCD or LED displays, plasma displays, cathode ray tubes(CRT), etc. A user input interface 822 is provided, including one ormore user interface mechanisms such as a mouse, keyboard, microphone,touch pad, touch screen, voice-recognition system, etc.

Server 801 may be coupled to other computing devices via a network. Theserver may be part of a larger network configuration as in a global areanetwork (GAN) such as the Internet 828.

As also will be appreciated by one skilled in the art, the exemplaryembodiments may be embodied in a wireless communication device, atelecommunication network, as a method or in a computer program product.Accordingly, the exemplary embodiments may take the form of an entirelyhardware embodiment or an embodiment combining hardware and softwareaspects. Further, the exemplary embodiments may take the form of acomputer program product stored on a computer-readable storage mediumhaving computer-readable instructions embodied in the medium. Anysuitable computer-readable medium may be utilized, including hard disks,CD-ROMs, digital versatile discs (DVD), optical storage devices, ormagnetic storage devices such as floppy disk or magnetic tape. Othernon-limiting examples of computer-readable media include flash-typememories or other known types of memories.

The disclosed exemplary embodiments provide an apparatus and a methodfor increasing similarity between a first seismic dataset and a secondseismic dataset from at least one seismic survey of a subsurface. Itshould be understood that this description is not intended to limit theinvention. On the contrary, the exemplary embodiments are intended tocover alternatives, modifications and equivalents, which are included inthe spirit and scope of the invention as defined by the appended claims.Further, in the detailed description of the exemplary embodiments,numerous specific details are set forth in order to provide acomprehensive understanding of the claimed invention. However, oneskilled in the art would understand that various embodiments may bepracticed without such specific details.

Although the features and elements of the present exemplary embodimentsare described in the embodiments in particular combinations, eachfeature or element can be used alone without the other features andelements of the embodiments or in various combinations with or withoutother features and elements disclosed herein.

This written description uses examples of the subject matter disclosedto enable any person skilled in the art to practice the same, includingmaking and using any devices or systems and performing any incorporatedmethods. The patentable scope of the subject matter is defined by theclaims, and may include other examples that occur to those skilled inthe art. Such other examples are intended to be within the scope of theclaims.

What is claimed is:
 1. A method for increasing similarity between afirst seismic dataset and a second seismic dataset from at least oneseismic survey of a subsurface, the method comprising: obtaining thefirst seismic dataset; obtaining the second seismic dataset; migratingthe first seismic dataset and second seismic dataset to a dip angleimage domain; and using the migrated first seismic dataset and themigrated second seismic dataset in the dip angle image domain tocalculate, with a processor, a set of decimating weights to be appliedto the first seismic dataset and the second seismic dataset to maximizea similarity between the first seismic dataset and the second seismicdataset.
 2. The method of claim 1, wherein the method further comprises:applying the decimated weights to the first seismic dataset and thesecond seismic dataset; and generating an image of the subsurface usingthe first seismic dataset and the second seismic dataset followingapplication of the decimated weights.
 3. The method of claim 1, wherein:the first seismic dataset comprises a plurality of first seismic tracesobtained from a base seismic survey and the second seismic datasetcomprises a plurality of second seismic traces obtained from a monitorseismic survey conducted after the base seismic survey; the methodfurther comprising selecting a subset of the first seismic traces and asubset of the second seismic traces; and migrating the first seismicdataset and second seismic dataset further comprises migrating thesubset of first seismic traces and the subset of second seismic traces.4. The method of claim 1, wherein the set of decimating weights iscalculated to be applied to the first seismic dataset and the secondseismic dataset in the dip angle image domain.
 5. The method of claim 1,wherein migrating the first seismic dataset and second seismic datasetto the dip angle image domain further comprises migrating the firstseismic dataset to a plurality of first seismic dataset dip anglepartial images and the second seismic dataset to a plurality of secondseismic dataset dip angle partial images, each first seismic dataset andsecond seismic dataset dip angle partial image associated with one of aplurality of dip angles.
 6. The method of claim 5, wherein using themigrated first seismic dataset and the migrated second seismic datasetin the dip angle image domain to calculate the set of decimating weightsfurther comprises determining a measure of similarity between a firstseismic dataset dip angle partial image and a second seismic dataset dipangle partial image for each pair of dip angle partial images associatedwith a common one of the plurality of dip angles.
 7. The method of claim6, wherein the measure of similarity comprises a point by point measureof similarity.
 8. The method of claim 6, wherein using the migratedfirst seismic dataset and the migrated second seismic dataset in the dipangle image domain to calculate the set of decimating weights furthercomprises calculating a set of decimating weights for the first seismicdataset dip angle partial image and the second seismic dataset dip anglepartial image in each pair of dip angle partial images.
 9. The method ofclaim 8, wherein each set of decimating weights when multiplied to thefirst seismic dataset dip angle partial image and the second seismicdataset dip angle partial image in a given pair of dip angle partialimages increase a similarity of the first seismic dataset dip anglepartial image and the second seismic dataset dip angle partial image inthat given pair of dip angle partial images.
 10. The method of claim 9,wherein the method further comprises: multiplying each set of decimatingweights to the first seismic dataset dip angle partial image and thesecond seismic dataset dip angle partial image in the pair of dip anglepartial images associated with that set of decimating weights; andcombining the first seismic dataset dip angle partial image and thesecond seismic dataset dip angle partial image in each pair of dip anglepartial images following multiplication to produce an image of thesubsurface.
 11. The method of claim 5, wherein the method furthercomprises selecting the plurality of dip angles by: identifying aplurality of frequency bands in the first and second seismic datasets;and associating a dip angle with each one of the plurality of frequencybands, wherein lower frequencies are associated with larger dip anglesand higher frequencies are associated with smaller dip angles.
 12. Themethod of claim 5, wherein the method further comprises selecting theplurality of dip angles by: identifying a smallest dip angle, thesmallest dip angle greater than zero; identifying a largest dip angle,the largest dip angle less than a maximum dip angle in the subsurface;and selecting the plurality of dip angles between the smallest dip angleand the largest dip angle.
 13. A computing device for increasingsimilarity between a first seismic dataset and a second seismic datasetfrom at least one seismic survey of a subsurface, the computing devicecomprising: a database comprising the first seismic dataset and thesecond seismic dataset; and a processor in communication with thedatabase and configured to: migrate the first seismic dataset and secondseismic dataset to a dip angle image domain; and use the migrated firstseismic dataset and the migrated second seismic dataset in the dip angleimage domain to calculate, with a processor, a set of decimating weightsto be applied to the first seismic dataset and the second seismicdataset to maximize a similarity between the first seismic dataset andthe second seismic dataset.
 14. The computing device of claim 13,wherein the processor is further configured to migrate the first seismicdataset to a plurality of first seismic dataset dip angle partial imagesand the second seismic dataset to a plurality of second seismic datasetdip angle partial images, each first seismic dataset and second seismicdataset dip angle partial image associated with one of a plurality ofdip angles.
 15. The computing device of claim 14, wherein the processoris further configured to determine a measure of similarity between afirst seismic dataset dip angle partial image and a second seismicdataset dip angle partial image for each pair of dip angle partialimages associated with a common one of the plurality of dip angles. 16.The computing device of claim 15, wherein the measure of similaritycomprises a point by point measure of similarity.
 17. The computingdevice of claim 15, wherein the processor is further configured tocalculate a set of decimating weights for the first seismic dataset dipangle partial image and the second seismic dataset dip angle partialimage in each pair of dip angle partial images.
 18. The computing deviceof claim 17, wherein each set of decimating weights when multiplied tothe first seismic dataset dip angle partial image and the second seismicdataset dip angle partial image in a given pair of dip angle partialimages increase a similarity of the first seismic dataset dip anglepartial image and the second seismic dataset dip angle partial image inthat given pair of dip angle partial images.
 19. The computing device ofclaim 18, wherein the processor is further configured to: multiply eachset of decimating weights to the first seismic dataset dip angle partialimage and the second seismic dataset dip angle partial image in the pairof dip angle partial images associated with that set of decimatingweights; and combine the first seismic dataset dip angle partial imageand the second seismic dataset dip angle partial image in each pair ofdip angle partial images following multiplication to produce an image ofthe subsurface.
 20. A non-transitory computer readable medium includingcomputer executable instructions, wherein the instructions, whenexecuted by a computer, implement a method for increasing similaritybetween a first seismic dataset and a second seismic dataset from atleast one seismic survey of a subsurface, the method comprising:obtaining the first seismic dataset; obtaining the second seismicdataset; migrating the first seismic dataset and second seismic datasetto a dip angle image domain; and using the migrated first seismicdataset and the migrated second seismic dataset in the dip angle imagedomain to calculate, with a processor, a set of decimating weights to beapplied to the first seismic dataset and the second seismic dataset tomaximize a similarity between the first seismic dataset and the secondseismic dataset.